Business Data Analytics BSc (Hons)
商业数据分析师理学学士(荣誉)本科
班戈大学的商业数据分析学位课程融合了商业与技术,为学生应对当今以数据为导向的就业市场做好准备。随着企业越来越依赖数据,本课程将为你提供扎实的商业知识和技术技能,使你能够收集、分析和解释数据,以支持决策制定。你将学习如何运用顶级银行和机构所使用的实际工具和软件来发现成本节约机会、提高效率、改善客户体验以及优化供应链。课程在支持性强且多元化的学习环境中进行,你将与来自世界各地的学生共同学习,获得全球化的经济洞察。大学的交易室让你能够访问英国及全球市场的实时和历史金融数据。课程由具备行业专长的经验丰富的讲师授课,并与欧洲中央银行、国际清算银行等主要机构保持紧密联系,确保教学内容具有实用性和相关性。
申请要求
中国学生需满足以下学术背景要求之一:成功通过高考;或高中毕业成绩良好并完成一年预科课程;或持有大专文凭(大学或学院颁发的2-3年制);或来自合作院校的毕业文凭;或IB文凭28-34分;或A-level成绩达112-136 UCAS分。若Level 3资格未体现数学能力,则需GCSE数学C/4分以上。
语言要求
雅思总分6.0,各单项不低于5.5。
| 科目 | 总分 | 阅读 | 听力 | 口语 | 写作 |
|---|
雅思 | 6.0 | 5.5 | 5.5 | 5.5 | 5.5 |
PTE学术英语考试总分不低于56,且各单项得分不低于51。
| 科目 | 总分 | 阅读 | 听力 | 口语 | 写作 |
|---|
PTE | 56 | 51 | 51 | 51 | 51 |
托福:总分4.0(最低单项分3.5)
| 科目 | 总分 | 阅读 | 听力 | 口语 | 写作 |
|---|
新托福 | 4 | 3.5 | 3.5 | 3.5 | 3.5 |
开学与申请日期
2027
| 开学日期 | 申请开始日期 | 申请截止日期 | 申请状态 |
|---|
| 2027-09 | 2026-09-01 | 2027-01-13 129天 | 开放中 |
2026
| 开学日期 | 申请开始日期 | 申请截止日期 | 申请状态 |
|---|
| 2026-01 | - | - NaN天 | - |
| 2026-09 | 2025-09-02 | 2026-01-14 0天 | 已截止 |
课程描述
Studying this Business Data Analytics degree will equip you with the knowledge and skills to analyse datasets in a variety of business contexts, learning the valuable techniques data analysts use to analyse data and extract insights.
Additionally, you will gain experience of essential tools, algorithms, and models, with opportunities to work with Python and R, two widely used coding platforms in the field.
Practical sessions on our trading floor allow you to apply your new-found knowledge on Stata and the Stocktrak global investment trading simulations, building skills in coding, modelling, analytical, numerical, data analysis and problem-solving. You will have access to the latest data from Bloomberg, S&P Capital IQ and Thomson Reuters.
This degree in business data analytics prepares you to become a strategic manager of data, using core managerial theories paired with practical analytical skills to uncover trends, patterns, and support organisational decision making.
As well as sharing findings from their own research, staff will encourage you to discuss and debate contemporary trends, such as reproducible analysis, potential bias in big data models, large language models, and the best way to visualise results for non-technical audiences.
You will purposefully be exposed to applications of data analytics to numerous settings including risk analytics, marketing analytics, computational finance, and customer analytics, throughout your degree. This ensures you discover which applications interest you the most for your future career.